A production-focused guide to building a RAG (Retrieval Augmented Generation) system on Cloudflare's edge infrastructure using Workers, Vectorize, and Workers AI. Covers the full pipeline: embedding documents with BGE, storing vectors in Vectorize, querying with semantic similarity, and generating grounded answers with Llama 3.3. Includes real cost data showing $8-10/month vs $25-70/month for traditional alternatives, performance benchmarks (~365ms for retrieval, 600-1600ms end-to-end), error handling, input sanitization, and production tips like chunking strategies, reranking, and streaming responses. No external API keys or paid vector database subscriptions required.
Table of contents
Table of ContentsWhat You Will BuildPrerequisitesHow RAG WorksHow to Set Up Your ProjectHow to Build the Data PipelineHow to Build the Query PipelineHow to Add Error Handling and SecurityPerformance and Cost AnalysisConclusionSort: